Geometric Data Analysis

by ; ;
Format: Hardcover
Pub. Date: 2004-10-01
Publisher(s): Kluwer Academic Pub
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Summary

Geometric Data Analysis (GDA) is the name suggested by P. Suppes (Stanford University) to designate the approach to Multivariate Statistics initiated by Benzécri as Correspondence Analysis, an approach that has become more and more used and appreciated over the years. This book presents the full formalization of GDA in terms of linear algebra - the most original and far-reaching consequential feature of the approach - and shows also how to integrate the standard statistical tools such as Analysis of Variance, including Bayesian methods. Chapter 9, Research Case Studies, is nearly a book in itself; it presents the methodology in action on three extensive applications, one for medicine, one from political science, and one from education (data borrowed from the Stanford computer-based Educational Program for Gifted Youth ). Thus the readership of the book concerns both mathematicians interested in the applications of mathematics, and researchers willing to master an exceptionally powerful approach of statistical data analysis.

Table of Contents

Foreword by Patrick Suppes vii
Preface ix
1 Overview of Geometric Data Analysis
1(22)
1.1 CA of a Historical Data Set
2(3)
1.2 The Three Key Ideas of GDA
5(5)
1.3 Three Paradigms of GDA
10(1)
1.4 Historical Sketch
11(3)
1.5 Methodological Strong Points
14(3)
1.6 From Descriptive to Inductive Analysis
17(3)
1.7 Organization of the Book
20(3)
2 Correspondence Analysis (CA)
23(52)
2.1 Measure vs Variable Duality
24(7)
2.2 Measure over a Cartesian Product
31(5)
2.3 Correspondence Analysis
36(23)
2.4 Extensions and Concluding Comments
59(6)
Exercises
65(10)
3 Euclidean Cloud
75(54)
3.1 Basic Statistics
76(3)
3.2 Projected Clouds
79(8)
3.3 Principal Directions
87(8)
3.4 Principal Hyperellipsoids
95(5)
3.5 Between and Within Clouds
100(6)
3.6 Euclidean Classification
106(10)
3.7 Matrix Formulas
116(13)
4 Principal Component Analysis (PCA)
129(50)
4.1 Biweighted PCA
132(17)
4.2 Simple PCA
149(1)
4.3 Standard PCA
150(3)
4.4 General PCA
153(2)
4.5 PCA of a Table of Measures
155(5)
4.6 Methodology of PCA
160(19)
5 Multiple Correspondence Analysis (MCA)
179(72)
5.1 Standard MCA
181(22)
5.2 Specific MCA
203(11)
5.3 Methodology of MCA
214(7)
5.4 The Culture Example
221(20)
Exercises
241(10)
6 Structured Data Analysis
251(18)
6.1 Structuring Factors
252(4)
6.2 Analysis of Comparisons
256(5)
6.3 Additive and Interaction Clouds
261(4)
6.4 Related Topics
265(4)
7 Stability of a Euclidean Cloud
269(28)
7.1 Stability and Grouping
270(7)
7.2 Influence of a Group of Points
277(4)
7.3 Change of Metric
281(2)
7.4 Influence of a Variable
283(8)
7.5 Basic Theorems
291(6)
8 Inductive Data Analysis
297(36)
8.1 Inference in Multivariate Statistics
298(3)
8.2 Univariate Effects
301(9)
8.3 Combinatorial Inference
310(6)
8.4 Bayesian Data Analysis
316(6)
8.5 Inductive GDA
322(9)
8.6 Guidelines for Inductive Analysis
331(2)
9 Research Case Studies
333(86)
9.1 Parkinson Study
336(29)
9.2 French Political Space
365(29)
9.3 EPGY Study
394(23)
9.4 About Software
417(2)
10 Mathematical Bases 419(32)
10.1 Matrix Operations
420(2)
10.2 Finite-dimensional Vector Space
422(6)
10.3 Euclidean Vector Space
428(7)
10.4 Multidimensional Geometry
435(7)
10.5 Spectral Theorem
442(9)
Bibliography 451(13)
Index 464
Name Index
464(3)
Symbol Index
467(2)
Subject Index
469

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